Information

Position Pipeline¶

In [ ]:

%reload_ext autoreload
%autoreload 2

%reload_ext autoreload %autoreload 2

This data pipeline takes the 2D position (in video pixels) of the green and red LEDs tracked by Trodes, and computes:

• head position (in cm)
• head orientation (in radians)
• head velocity (in cm/s)
• head speed (in cm/s)

We can then check the quality of the head position and direction by plotting the them on the video along with the oringal green and red LEDs.

This notebook will take you through this process for one dataset.

1. Loading the session data¶

First let us make sure that the session we want to analyze is inserted into the database:

In [2]:

import spyglass as nd

nwb_file_name = "chimi20200216_new.nwb"

nd.insert_sessions(nwb_file_name)

import spyglass as nd nwb_file_name = "chimi20200216_new.nwb" nd.insert_sessions(nwb_file_name)

[2022-08-04 16:16:09,213][INFO]: Connecting zoldello@lmf-db.cin.ucsf.edu:3306
[2022-08-04 16:16:09,264][INFO]: Connected zoldello@lmf-db.cin.ucsf.edu:3306

/home/zoldello/anaconda3/envs/spyglass/lib/python3.9/site-packages/position_tools/core.py:3: DeprecationWarning: Please use `gaussian_filter1d` from the `scipy.ndimage` namespace, the `scipy.ndimage.filters` namespace is deprecated.
  from scipy.ndimage.filters import gaussian_filter1d

After insert, a new nwb file is made that is a copy of the original version without the electrophysiological data. This name is used as a key in the downstream data analysis so we save the name of the copied version for later use.

In [3]:

from spyglass.common.nwb_helper_fn import get_nwb_copy_filename

nwb_copy_file_name = get_nwb_copy_filename(nwb_file_name)
nwb_copy_file_name

from spyglass.common.nwb_helper_fn import get_nwb_copy_filename nwb_copy_file_name = get_nwb_copy_filename(nwb_file_name) nwb_copy_file_name

Out[3]:

'chimi20200216_new_.nwb'

2. Setting the parameters for running the position pipeline¶

The parameters for the position pipeline are set by the PositionInfoParameters table. default is the name of the standard set of parameters. As usual, if you want to change the parameters, you can insert your own into the table.

The parameters are as follows:

• max_separation is the maxmium acceptable distance (in cm) between the red and green LEDs. When the distance between the LEDs becomes greater than this number, the times are marked as NaNs and inferred by interpolation. This is useful parameter when the inferred red or green LED position tracks a reflection instead of the true red or green LED position. It is set to 9.0 cm by default.
• max_speed is the maximum plausible speed (in cm/s) the animal can move at. Times when the speed is greater than this threshold are marked as NaNs and inferred by interpolation. This can be useful in preventing big, sudden jumps in position. It is set to 300.0 cm/s by default.
• position_smoothing_duration controls how much the red and green LEDs are smoothed before computing the average of their position to get the head position. It is in units of seconds.
• speed_smoothing_std_dev controls how much the head speed is smoothed. It corresponds to the standard deviation of the Gaussian kernel used to smooth the speed and is in units of seconds. It is set to 0.100 seconds by default.
• front_led1 is either 1 or 0 indicating True or False. It controls which LED is treated as the front LED and the back LED, which is important for calculating the head direction.
  • 1 indicates that the LED corresponding to xloc, yloc in the RawPosition table as the front LED and the LED corresponding to xloc2, yloc2 as the back LED.
  • 0 indicates that xloc, yloc are treated as the back LED and xloc2, yloc2 are treated as the front LED.

In [4]:

from spyglass.common.common_position import PositionInfoParameters

PositionInfoParameters()

from spyglass.common.common_position import PositionInfoParameters PositionInfoParameters()

Out[4]:

position_info_param_name name for this set of parameters	max_separation max distance (in cm) between head LEDs	max_speed max speed (in cm / s) of animal	position_smoothing_duration size of moving window (in seconds)	speed_smoothing_std_dev smoothing standard deviation (in seconds)	head_orient_smoothing_std_dev smoothing std deviation (in seconds)	led1_is_front first LED is front LED and second is back LED, else first LED is back	is_upsampled upsample the position to higher sampling rate	upsampling_sampling_rate The rate to be upsampled to	upsampling_interpolation_method see pandas.DataFrame.interpolation for list of methods
default	9.0	300.0	0.125	0.1	0.001	1	0	nan	linear
default_decoding	9.0	300.0	0.125	0.1	0.001	1	1	500.0	linear
default_lfp	9.0	300.0	0.125	0.1	0.001	1	1	1000.0	linear

Total: 3

In [5]:

PositionInfoParameters.insert1(
    {"position_info_param_name": "default"}, skip_duplicates=True
)
PositionInfoParameters()

PositionInfoParameters.insert1( {"position_info_param_name": "default"}, skip_duplicates=True ) PositionInfoParameters()

Out[5]:

position_info_param_name name for this set of parameters	max_separation max distance (in cm) between head LEDs	max_speed max speed (in cm / s) of animal	position_smoothing_duration size of moving window (in seconds)	speed_smoothing_std_dev smoothing standard deviation (in seconds)	head_orient_smoothing_std_dev smoothing std deviation (in seconds)	led1_is_front first LED is front LED and second is back LED, else first LED is back	is_upsampled upsample the position to higher sampling rate	upsampling_sampling_rate The rate to be upsampled to	upsampling_interpolation_method see pandas.DataFrame.interpolation for list of methods
default	9.0	300.0	0.125	0.1	0.001	1	0	nan	linear
default_decoding	9.0	300.0	0.125	0.1	0.001	1	1	500.0	linear
default_lfp	9.0	300.0	0.125	0.1	0.001	1	1	1000.0	linear

Total: 3

Now that you have set the parameters, you need to associate those parameters with a partiuclar nwb file and interval using the IntervalPositionInfoSelection table in order to run the position pipeline.

Let's look at the intervals for chimi20200216_new.nwb first. We care about the intervals that start with pos and ending with valid times.

In [6]:

import pandas as pd

pd.DataFrame(nd.common.IntervalList & {"nwb_file_name": nwb_copy_file_name})

import pandas as pd pd.DataFrame(nd.common.IntervalList & {"nwb_file_name": nwb_copy_file_name})

Out[6]:

	nwb_file_name	interval_list_name	valid_times
0	chimi20200216_new_.nwb	01_s1	[[1581884110.9124804, 1581886102.599356]]
1	chimi20200216_new_.nwb	02_r1	[[1581886847.561317, 1581888227.5987928]]
2	chimi20200216_new_.nwb	02_r1 noPrePostTrialTimes	[[1581886896.4185014, 1581888227.5987928]]
3	chimi20200216_new_.nwb	03_s2	[[1581888339.9145422, 1581890273.3987277]]
4	chimi20200216_new_.nwb	04_r2	[[1581890568.8618634, 1581891934.598601]]
...	...	...	...
169	chimi20200216_new_.nwb	R_fabb5ef8_ampl_2000_prop_75_artifact_removed_...	[[1581886896.4185073, 1581888227.5987911]]
170	chimi20200216_new_.nwb	sleeps	[[1581884110.9124804, 1581886102.599356], [158...
171	chimi20200216_new_.nwb	sleeps_runs_noPrePostTrialTimes	[[1581884110.9124804, 1581886102.599356], [158...
172	chimi20200216_new_.nwb	sleeps_runs_noPrePostTrialTimes raw data valid...	[[1581884110.9124804, 1581886102.599356], [158...
173	chimi20200216_new_.nwb	sleeps_runs_noPrePostTrialTimes raw data valid...	[[1581884110.9134343, 1581886102.599356], [158...

174 rows × 3 columns

Let's choose the interval corresponding to pos 1 valid times in chimi20200216_new_.nwb.

We first look at the "raw" position data now to see the input into the pipeline. The raw position is in the RawPosition table and corresponds to the inferred position of the red and green LEDs from the video (using an algorithm in Trodes). It is in units of pixels. The number of time points corresponds to when the position tracking was turned on and off (and so may not have the same number of time points as the video itself).

We can retrieve the data in the RawPosition table for a given interval using a special method called fetch1_dataframe. It returns the position of the red and green LEDs as a pandas dataframe where time is the index. The columns of the dataframe are:

• xloc, yloc are the x- and y-position of one of the LEDs
• xloc2, yloc2 are the x- and y-position of the other LEDs.

In [7]:

from spyglass.common.common_behav import RawPosition

raw_position_df = (
    RawPosition()
    & {
        "nwb_file_name": nwb_copy_file_name,
        "interval_list_name": "pos 1 valid times",
    }
).fetch1_dataframe()
raw_position_df

from spyglass.common.common_behav import RawPosition raw_position_df = ( RawPosition() & { "nwb_file_name": nwb_copy_file_name, "interval_list_name": "pos 1 valid times", } ).fetch1_dataframe() raw_position_df

[2022-08-04 16:16:12,541][WARNING]: Skipped checksum for file with hash: 050179d6-42a0-9236-45ee-6069346d0196, and path: /stelmo/nwb/raw/chimi20200216_new_.nwb

/stelmo/nwb/raw/chimi20200216_new_.nwb

Out[7]:

	xloc	yloc	xloc2	yloc2
time
1.581887e+09	264	638	285	635
1.581887e+09	264	672	269	635
1.581887e+09	264	638	280	642
1.581887e+09	268	635	284	636
1.581887e+09	268	674	283	636
...	...	...	...	...
1.581888e+09	556	598	543	616
1.581888e+09	555	598	543	616
1.581888e+09	556	598	543	616
1.581888e+09	555	598	543	616
1.581888e+09	556	598	542	615

39340 rows × 4 columns

Let's just quickly plot the two LEDs to get a sense of the inputs to the pipeline:

In [8]:

import matplotlib.pyplot as plt

fig, ax = plt.subplots(1, 1, figsize=(10, 10))
ax.plot(raw_position_df.xloc, raw_position_df.yloc, color="green")
ax.plot(raw_position_df.xloc2, raw_position_df.yloc2, color="red")
ax.set_xlabel("x-position [pixels]", fontsize=18)
ax.set_ylabel("y-position [pixels]", fontsize=18)
ax.set_title("Raw Position", fontsize=28)

import matplotlib.pyplot as plt fig, ax = plt.subplots(1, 1, figsize=(10, 10)) ax.plot(raw_position_df.xloc, raw_position_df.yloc, color="green") ax.plot(raw_position_df.xloc2, raw_position_df.yloc2, color="red") ax.set_xlabel("x-position [pixels]", fontsize=18) ax.set_ylabel("y-position [pixels]", fontsize=18) ax.set_title("Raw Position", fontsize=28)

Out[8]:

Text(0.5, 1.0, 'Raw Position')

Okay, now that we understand what the inputs to the pipeline are, let's associate a set of parameters with a given interval.

To associate parameters with a given interval, we insert them into the IntervalPositionInfoSelection table.

Here we associate the with the default position info parameters with the interval pos 1 valid times:

In [9]:

from spyglass.common.common_position import IntervalPositionInfoSelection

IntervalPositionInfoSelection.insert1(
    {
        "nwb_file_name": nwb_copy_file_name,
        "interval_list_name": "pos 1 valid times",
        "position_info_param_name": "default",
    },
    skip_duplicates=True,
)

from spyglass.common.common_position import IntervalPositionInfoSelection IntervalPositionInfoSelection.insert1( { "nwb_file_name": nwb_copy_file_name, "interval_list_name": "pos 1 valid times", "position_info_param_name": "default", }, skip_duplicates=True, )

Now let's check to see if we've inserted the parameters correctly:

In [10]:

IntervalPositionInfoSelection()

IntervalPositionInfoSelection()

Out[10]:

position_info_param_name name for this set of parameters	nwb_file_name name of the NWB file	interval_list_name descriptive name of this interval list
default_decoding	CH6120211203_.nwb	pos 0 valid times
default_decoding	CH6520211201_.nwb	pos 0 valid times
default	chimi20200216_new_.nwb	pos 1 valid times
default_decoding	chimi20200216_new_.nwb	pos 1 valid times
default_lfp	chimi20200216_new_.nwb	pos 1 valid times
default	fern20211007_.nwb	pos 0 valid times
default_decoding	fern20211007_.nwb	pos 0 valid times
default	fern20211007_.nwb	pos 1 valid times
default_decoding	fern20211007_.nwb	pos 1 valid times
default	fern20211007_.nwb	pos 10 valid times
default_decoding	fern20211007_.nwb	pos 10 valid times
default	fern20211007_.nwb	pos 11 valid times

...

Total: 1814

3. Running the position pipeline and retrieving the results¶

Now that we have associated the parameters with the interval we want to run, we can finally run the pipeline for that interval.

We run the pipeline using the populate method on the IntervalPositionInfo table.

In [11]:

from spyglass.common.common_position import IntervalPositionInfo

IntervalPositionInfo.populate()

from spyglass.common.common_position import IntervalPositionInfo IntervalPositionInfo.populate()

We can see that each NWB file, interval, and parameter set is now associated with a newly created analysis NWB file and object IDs that correspond to our newly computed data. This isn't as useful to work with so we will use another method below to actually retrieve the data for a given interval.

In [12]:

IntervalPositionInfo()

IntervalPositionInfo()

Out[12]:

position_info_param_name name for this set of parameters	nwb_file_name name of the NWB file	interval_list_name descriptive name of this interval list	analysis_file_name name of the file	head_position_object_id	head_orientation_object_id	head_velocity_object_id
default	chimi20200216_new_.nwb	pos 1 valid times	chimi20200216_new_ZF24K2JHC1.nwb	92a087ca-3df6-49f9-b544-b304dc4a6f26	65199939-b878-457e-836a-98da1ffbf244	a876e869-4eb5-4c4b-9ff5-a50914ff1b45
default	fern20211007_.nwb	pos 0 valid times	fern20211007_0V93CW9TIT.nwb	f4a20aa2-20a0-4a73-b42d-5a96fff32a13	e835e832-86b0-41a3-9d8e-bf87b5c55e9d	d1e0ae3c-4c05-47e2-89fb-3951d70eece9
default	fern20211007_.nwb	pos 1 valid times	fern20211007_GF77PRAGTB.nwb	ae3317fa-bbd3-4a3a-9279-af969647972f	eedee7f8-290b-4a22-92bd-564f83fde9b6	86db909f-fb0d-4db5-883b-b1e20c41a72d
default	fern20211007_.nwb	pos 10 valid times	fern20211007_I01IPA5QPF.nwb	8b7722cb-fa73-4d34-9617-86a193218bfd	74d5e5a8-d736-4fd0-ad73-fcdf644b5fdf	7e67fbee-f766-4658-be2b-e8da9a377796
default	fern20211007_.nwb	pos 11 valid times	fern20211007_504348DO5F.nwb	c294ea11-10a8-46a7-8e03-b10859783239	a703e06b-b4f9-42e6-8e19-b0366e606de3	228da90d-3442-40e0-8311-bdd08b3aa647
default	fern20211007_.nwb	pos 12 valid times	fern20211007_YJ7FEZ5V4M.nwb	e8b7017f-745e-4131-923b-3afaf5f62a9b	db509824-cd50-4f4a-9a0c-3b7e766daac1	85064b3e-2cb7-4723-80bd-9c0e36141d6a
default	fern20211007_.nwb	pos 13 valid times	fern20211007_0HXO9LW0QP.nwb	62c1dbc6-8d03-4d02-9e7a-185edb796182	11734fa1-27c8-41d3-9970-b0db9239b5a8	919e565b-7937-488c-a484-c745fe9f4a52
default	fern20211007_.nwb	pos 14 valid times	fern20211007_E8IV6HVDZL.nwb	93e1a36b-bd8a-45ec-a4c5-0d42c411c63a	f5796260-9bb1-41a5-b272-ac51c645e781	3bac5d8d-d103-4aa8-925e-bb968558b9f3
default	fern20211007_.nwb	pos 15 valid times	fern20211007_BN0LH7DV4K.nwb	c0ead339-9380-4201-b0ab-d694e440425f	36c65b2e-a2db-414e-bbe6-9a0d851a79b2	d122d225-a1ba-494f-9ca7-9d8cbb0411f6
default	fern20211007_.nwb	pos 16 valid times	fern20211007_WRBSHYU23X.nwb	71d010f3-9e46-400c-92e5-04d48829cb47	2c3542ee-18fb-4331-8aff-f514258530e0	842bbba1-6e57-47ed-afcb-7ba84d4033af
default	fern20211007_.nwb	pos 17 valid times	fern20211007_JJHMRY754E.nwb	d82418cf-98ca-43e1-ac3c-c08bd28fa70f	6e19cd2b-f134-4f73-9dae-12d7296c58f5	e100713e-5cdb-42c5-bc9f-60df76aa26b0
default	fern20211007_.nwb	pos 18 valid times	fern20211007_6CBPQXBDFJ.nwb	edb09afb-62e1-4948-b62c-e94254275845	6c488035-0709-482d-99ef-c4c698d0e0cd	9a0d60f8-60d4-436d-940a-5c5fa83d9d76

...

Total: 1814

In order to retrieve the results of the computation, we use a special method called fetch1_dataframe from the IntervalPositionInfo table that will retrieve the position pipeline results as a pandas DataFrame. Time is set as the index of the dataframe.

This will only work for a single interval so we need to specify the NWB file and the interval.

This dataframe has the following columns:

• head_position_x, head_position_y: the x,y position of the head position (in cm).
• head_orientation: The direction of the head relative to the bottom left corner (in radians)
• head_velocity_x, head_velocity_y: the directional change in head position over time (in cm/s)
• head_speed: the magnitude of the change in head position over time (in cm/s)

In [13]:

position_info = (
    IntervalPositionInfo()
    & {
        "nwb_file_name": nwb_copy_file_name,
        "interval_list_name": "pos 1 valid times",
        "position_info_param_name": "default",
    }
).fetch1_dataframe()
position_info

position_info = ( IntervalPositionInfo() & { "nwb_file_name": nwb_copy_file_name, "interval_list_name": "pos 1 valid times", "position_info_param_name": "default", } ).fetch1_dataframe() position_info

/stelmo/nwb/analysis/chimi20200216_new_ZF24K2JHC1.nwb

Out[13]:

	head_position_x	head_position_y	head_orientation	head_velocity_x	head_velocity_y	head_speed
time
1.581887e+09	91.051650	211.127050	2.999696	1.387074	2.848838	3.168573
1.581887e+09	90.844337	211.417287	3.078386	3.123201	3.411111	4.624939
1.581887e+09	90.637025	211.707525	-3.114572	5.431643	4.089597	6.799085
1.581887e+09	90.802875	211.596958	-3.033109	8.097753	4.979262	9.506138
1.581887e+09	91.288579	211.482443	-3.062550	10.840482	6.071373	12.424880
...	...	...	...	...	...	...
1.581888e+09	182.158583	201.452467	-0.986926	0.348276	0.218575	0.411182
1.581888e+09	182.158583	201.397183	-0.978610	0.279135	-0.058413	0.285182
1.581888e+09	182.213867	201.341900	-0.957589	0.193798	-0.283200	0.343162
1.581888e+09	182.158583	201.341900	-0.970083	0.110838	-0.417380	0.431846
1.581888e+09	182.158583	201.286617	-0.936414	0.045190	-0.453966	0.456209

39340 rows × 6 columns

If you are not familiar with pandas, the time variable is set as the index. It can be accessed using .index on the dataframe.

In [14]:

position_info.index

position_info.index

Out[14]:

Float64Index([1581886916.3153033, 1581886916.3486283, 1581886916.3819742,
              1581886916.4152992,  1581886916.448645, 1581886916.4819698,
              1581886916.5152948, 1581886916.5486405, 1581886916.5819652,
              1581886916.6152902,
              ...
              1581888227.3021932,  1581888227.335518,  1581888227.368864,
              1581888227.4021888,  1581888227.435535, 1581888227.4688597,
              1581888227.5021844, 1581888227.5355306, 1581888227.5688553,
                1581888227.60218],
             dtype='float64', name='time', length=39340)

4. Examining the results¶

We should always spot check our results to verify that the pipeline worked correctly.

Plots¶

Let's plot some of the variables first:

In [15]:

fig, ax = plt.subplots(1, 1, figsize=(10, 10))
ax.plot(position_info.head_position_x, position_info.head_position_y)
ax.set_xlabel("x-position [cm]", fontsize=18)
ax.set_ylabel("y-position [cm]", fontsize=18)
ax.set_title("Head Position", fontsize=28)

fig, ax = plt.subplots(1, 1, figsize=(10, 10)) ax.plot(position_info.head_position_x, position_info.head_position_y) ax.set_xlabel("x-position [cm]", fontsize=18) ax.set_ylabel("y-position [cm]", fontsize=18) ax.set_title("Head Position", fontsize=28)

Out[15]:

Text(0.5, 1.0, 'Head Position')

In [16]:

fig, ax = plt.subplots(1, 1, figsize=(10, 10))
ax.plot(position_info.head_velocity_x, position_info.head_velocity_y)
ax.set_xlabel("x-velocity [cm/s]", fontsize=18)
ax.set_ylabel("y-velocity [cm/s]", fontsize=18)
ax.set_title("Head Velocity", fontsize=28)

fig, ax = plt.subplots(1, 1, figsize=(10, 10)) ax.plot(position_info.head_velocity_x, position_info.head_velocity_y) ax.set_xlabel("x-velocity [cm/s]", fontsize=18) ax.set_ylabel("y-velocity [cm/s]", fontsize=18) ax.set_title("Head Velocity", fontsize=28)

Out[16]:

Text(0.5, 1.0, 'Head Velocity')

In [17]:

fig, ax = plt.subplots(1, 1, figsize=(25, 3))
ax.plot(position_info.index, position_info.head_speed)
ax.set_xlabel("Time", fontsize=18)
ax.set_ylabel("Speed [cm/s]", fontsize=18)
ax.set_title("Head Speed", fontsize=28)
ax.set_xlim((position_info.index.min(), position_info.index.max()))

fig, ax = plt.subplots(1, 1, figsize=(25, 3)) ax.plot(position_info.index, position_info.head_speed) ax.set_xlabel("Time", fontsize=18) ax.set_ylabel("Speed [cm/s]", fontsize=18) ax.set_title("Head Speed", fontsize=28) ax.set_xlim((position_info.index.min(), position_info.index.max()))

Out[17]:

(1581886916.3153033, 1581888227.60218)

Video¶

These look reasonable but it is better to evaluate these variables by plotting the results on the video where we can see how they correspond.

The video will appear in the current working directory when it is done.

In [24]:

from spyglass.common.common_position import PositionVideo

PositionVideo().make(
    {
        "nwb_file_name": nwb_copy_file_name,
        "interval_list_name": "pos 1 valid times",
        "position_info_param_name": "default",
    }
)

from spyglass.common.common_position import PositionVideo PositionVideo().make( { "nwb_file_name": nwb_copy_file_name, "interval_list_name": "pos 1 valid times", "position_info_param_name": "default", } )

[2022-08-04 15:05:53,003][WARNING]: Skipped checksum for file with hash: 050179d6-42a0-9236-45ee-6069346d0196, and path: /stelmo/nwb/raw/chimi20200216_new_.nwb

Loading position data...
Loading video data...

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
Input In [24], in <cell line: 3>()
      1 from spyglass.common.common_position import PositionVideo
----> 3 PositionVideo().make({'nwb_file_name': nwb_copy_file_name,
      4                       'interval_list_name': 'pos 1 valid times',
      5                       'position_info_param_name': 'default'
      6                      })

File ~/Documents/code/spyglass/src/spyglass/common/common_position.py:631, in PositionVideo.make(self, key)
    628 io = pynwb.NWBHDF5IO('/stelmo/nwb/raw/' +
    629                      video_info['nwb_file_name'], 'r')
    630 nwb_file = io.read()
--> 631 nwb_video = nwb_file.objects[video_info['video_file_object_id']]
    632 video_filename = nwb_video.external_file.value[0]
    634 nwb_base_filename = key['nwb_file_name'].replace('.nwb', '')

File ~/anaconda3/envs/spyglass/lib/python3.9/site-packages/hdmf/utils.py:932, in LabelledDict.__getitem__(self, args)
    930         return super().__getitem__(val)
    931 else:
--> 932     return super().__getitem__(key)

KeyError: '3234c1e5-c992-4fb4-9c82-d1603d90bce7'

4. Upsampling position data¶

Sometimes you need the position data to be in a different rate than it is sampled in, such as when decoding in 2 ms time bins. You can use the upsampling parameters to get this data:

• is_upsampled controls whether upsampling happens. If it is 1 then there is upsampling, and if it is 0 then upsampling does not happen.
• upsampling_sampling_rate is the rate you want to upsample to. For example position is typically recorded at 33 frames per seconds and you may want to upsample up to 500 frames per second.
• upsampling_interpolation_method is the interpolation method used for upsampling. It is set to linear by default. See the methods available for pandas.DataFrame.interpolate to get a list of the methods.

In [25]:

PositionInfoParameters.insert1(
    {
        "position_info_param_name": "default_decoding",
        "is_upsampled": 1,
        "upsampling_sampling_rate": 500,
    },
    skip_duplicates=True,
)

PositionInfoParameters()

PositionInfoParameters.insert1( { "position_info_param_name": "default_decoding", "is_upsampled": 1, "upsampling_sampling_rate": 500, }, skip_duplicates=True, ) PositionInfoParameters()

Out[25]:

position_info_param_name name for this set of parameters	max_separation max distance (in cm) between head LEDs	max_speed max speed (in cm / s) of animal	position_smoothing_duration size of moving window (in seconds)	speed_smoothing_std_dev smoothing standard deviation (in seconds)	head_orient_smoothing_std_dev smoothing std deviation (in seconds)	led1_is_front first LED is front LED and second is back LED, else first LED is back	is_upsampled upsample the position to higher sampling rate	upsampling_sampling_rate The rate to be upsampled to	upsampling_interpolation_method see pandas.DataFrame.interpolation for list of methods
default	9.0	300.0	0.125	0.1	0.001	1	0	nan	linear
default_decoding	9.0	300.0	0.125	0.1	0.001	1	1	500.0	linear
default_lfp	9.0	300.0	0.125	0.1	0.001	1	1	1000.0	linear

Total: 3

In [26]:

IntervalPositionInfoSelection.insert1(
    {
        "nwb_file_name": nwb_copy_file_name,
        "interval_list_name": "pos 1 valid times",
        "position_info_param_name": "default_decoding",
    },
    skip_duplicates=True,
)

IntervalPositionInfoSelection()

IntervalPositionInfoSelection.insert1( { "nwb_file_name": nwb_copy_file_name, "interval_list_name": "pos 1 valid times", "position_info_param_name": "default_decoding", }, skip_duplicates=True, ) IntervalPositionInfoSelection()

Out[26]:

position_info_param_name name for this set of parameters	nwb_file_name name of the NWB file	interval_list_name descriptive name of this interval list
default_decoding	CH6120211203_.nwb	pos 0 valid times
default_decoding	CH6520211125_.nwb	pos 0 valid times
default_decoding	CH6520211201_.nwb	pos 0 valid times
default	chimi20200216_new_.nwb	pos 1 valid times
default_decoding	chimi20200216_new_.nwb	pos 1 valid times
default_lfp	chimi20200216_new_.nwb	pos 1 valid times
default	fern20211007_.nwb	pos 0 valid times
default_decoding	fern20211007_.nwb	pos 0 valid times
default	fern20211007_.nwb	pos 1 valid times
default_decoding	fern20211007_.nwb	pos 1 valid times
default	fern20211007_.nwb	pos 10 valid times
default_decoding	fern20211007_.nwb	pos 10 valid times

...

Total: 1815

In [27]:

IntervalPositionInfo.populate()

IntervalPositionInfo.populate()

In [28]:

upsampled_position_info = (
    IntervalPositionInfo()
    & {
        "nwb_file_name": nwb_copy_file_name,
        "interval_list_name": "pos 1 valid times",
        "position_info_param_name": "default_decoding",
    }
).fetch1_dataframe()

upsampled_position_info

upsampled_position_info = ( IntervalPositionInfo() & { "nwb_file_name": nwb_copy_file_name, "interval_list_name": "pos 1 valid times", "position_info_param_name": "default_decoding", } ).fetch1_dataframe() upsampled_position_info

/stelmo/nwb/analysis/chimi20200216_new_6YC9LPAR7S.nwb

Out[28]:

	head_position_x	head_position_y	head_orientation	head_velocity_x	head_velocity_y	head_speed
time
1.581887e+09	91.051650	211.127050	2.680048	1.741550	2.301478	2.886139
1.581887e+09	91.039455	211.144123	3.003241	1.827555	2.333931	2.964320
1.581887e+09	91.027260	211.161196	3.008398	1.915800	2.366668	3.044898
1.581887e+09	91.015065	211.178268	3.012802	2.006286	2.399705	3.127901
1.581887e+09	91.002871	211.195341	3.017242	2.099012	2.433059	3.213352
...	...	...	...	...	...	...
1.581888e+09	182.158583	201.299625	-0.944304	0.057520	-0.356012	0.360629
1.581888e+09	182.158583	201.296373	-0.942329	0.053954	-0.356343	0.360404
1.581888e+09	182.158583	201.293121	-0.940357	0.050477	-0.356407	0.359964
1.581888e+09	182.158583	201.289869	-0.953059	0.047091	-0.356212	0.359312
1.581888e+09	182.158583	201.286617	-0.588081	0.043796	-0.355764	0.358450

655645 rows × 6 columns

In [24]:

fig, axes = plt.subplots(
    1, 2, figsize=(20, 10), sharex=True, sharey=True, constrained_layout=True
)
axes[0].plot(position_info.head_position_x, position_info.head_position_y)
axes[0].set_xlabel("x-position [cm]", fontsize=18)
axes[0].set_ylabel("y-position [cm]", fontsize=18)
axes[0].set_title("Head Position", fontsize=28)

axes[1].plot(
    upsampled_position_info.head_position_x,
    upsampled_position_info.head_position_y,
)
axes[1].set_xlabel("x-position [cm]", fontsize=18)
axes[1].set_ylabel("y-position [cm]", fontsize=18)
axes[1].set_title("Upsampled Head Position", fontsize=28)

fig, axes = plt.subplots( 1, 2, figsize=(20, 10), sharex=True, sharey=True, constrained_layout=True ) axes[0].plot(position_info.head_position_x, position_info.head_position_y) axes[0].set_xlabel("x-position [cm]", fontsize=18) axes[0].set_ylabel("y-position [cm]", fontsize=18) axes[0].set_title("Head Position", fontsize=28) axes[1].plot( upsampled_position_info.head_position_x, upsampled_position_info.head_position_y, ) axes[1].set_xlabel("x-position [cm]", fontsize=18) axes[1].set_ylabel("y-position [cm]", fontsize=18) axes[1].set_title("Upsampled Head Position", fontsize=28)

Out[24]:

Text(0.5, 1.0, 'Upsampled Head Position')

In [25]:

fig, axes = plt.subplots(
    2, 1, figsize=(25, 6), sharex=True, sharey=True, constrained_layout=True
)
axes[0].plot(position_info.index, position_info.head_speed)
axes[0].set_xlabel("Time", fontsize=18)
axes[0].set_ylabel("Speed [cm/s]", fontsize=18)
axes[0].set_title("Head Speed", fontsize=28)
axes[0].set_xlim((position_info.index.min(), position_info.index.max()))

axes[1].plot(upsampled_position_info.index, upsampled_position_info.head_speed)
axes[1].set_xlabel("Time", fontsize=18)
axes[1].set_ylabel("Speed [cm/s]", fontsize=18)
axes[1].set_title("Upsampled Head Speed", fontsize=28)

fig, axes = plt.subplots( 2, 1, figsize=(25, 6), sharex=True, sharey=True, constrained_layout=True ) axes[0].plot(position_info.index, position_info.head_speed) axes[0].set_xlabel("Time", fontsize=18) axes[0].set_ylabel("Speed [cm/s]", fontsize=18) axes[0].set_title("Head Speed", fontsize=28) axes[0].set_xlim((position_info.index.min(), position_info.index.max())) axes[1].plot(upsampled_position_info.index, upsampled_position_info.head_speed) axes[1].set_xlabel("Time", fontsize=18) axes[1].set_ylabel("Speed [cm/s]", fontsize=18) axes[1].set_title("Upsampled Head Speed", fontsize=28)

Out[25]:

Text(0.5, 1.0, 'Upsampled Head Speed')

In [26]:

fig, axes = plt.subplots(
    1, 2, figsize=(20, 10), sharex=True, sharey=True, constrained_layout=True
)
axes[0].plot(position_info.head_velocity_x, position_info.head_velocity_y)
axes[0].set_xlabel("x-velocity [cm/s]", fontsize=18)
axes[0].set_ylabel("y-velocity [cm/s]", fontsize=18)
axes[0].set_title("Head Velocity", fontsize=28)

axes[1].plot(
    upsampled_position_info.head_velocity_x,
    upsampled_position_info.head_velocity_y,
)
axes[1].set_xlabel("x-velocity [cm/s]", fontsize=18)
axes[1].set_ylabel("y-velocity [cm/s]", fontsize=18)
axes[1].set_title("Upsampled Head Velocity", fontsize=28)

fig, axes = plt.subplots( 1, 2, figsize=(20, 10), sharex=True, sharey=True, constrained_layout=True ) axes[0].plot(position_info.head_velocity_x, position_info.head_velocity_y) axes[0].set_xlabel("x-velocity [cm/s]", fontsize=18) axes[0].set_ylabel("y-velocity [cm/s]", fontsize=18) axes[0].set_title("Head Velocity", fontsize=28) axes[1].plot( upsampled_position_info.head_velocity_x, upsampled_position_info.head_velocity_y, ) axes[1].set_xlabel("x-velocity [cm/s]", fontsize=18) axes[1].set_ylabel("y-velocity [cm/s]", fontsize=18) axes[1].set_title("Upsampled Head Velocity", fontsize=28)

Out[26]:

Text(0.5, 1.0, 'Upsampled Head Velocity')

In [ ]: